Tiriamasis

library(tidyverse)
library(highcharter)
library(hrbrthemes)
library(ggrepel)
library(countrycode)


x<- readxl::read_xlsx("global_mortality.xlsx")
x<- x %>% 
  mutate(across(where(is.numeric),~round(.x,2)))
x <- x %>% janitor::clean_names(case="sentence")
names(x)[4:35]<-c("Širdies ligos","Vėžiai","Kvepavimo ligos","Diabetas","Demencija","Apatinių kvepavimo takų infekcijos",
  "Naujagimių mirtys","Viduriavimo ligos","Kelių įvykiai","Inkstų ligos","Tuberkuliozė","Kepenų ligos","Virškinimo ligos","AIDS",
  "Savižudybė","Maliarija","Žmogžudystės","Neprievalgis","Meningitas","Proteinų trūkumas","Skendimas","Mirtys gimdant","Parkinsono liga",
  "Alkoholis","Virškinimo infekcijos","Narkotikai","Hepatitas","Gaisras","Šaltis arba karštis","Gamtos katastrofos","Konfliktai","Terorizmas")

urlico <- "url(https://raw.githubusercontent.com/tugmaks/flags/2d15d1870266cf5baefb912378ecfba418826a79/flags/flags-iso/flat/24/%s.png)"

x_1<- x %>% mutate(countrycode = countrycode(Country, origin = "country.name", destination = "iso2c")) %>%
    mutate(marker = sprintf(urlico, countrycode),
         marker = map(marker, function(x) list(symbol = x)),
         flagicon = sprintf(urlico, countrycode),
         flagicon = str_replace_all(flagicon, "url\\(|\\)", ""),
         continent = countrycode(Country,origin="country.name",destination="continent"),
         CountryLT = countrycode(Country,origin="country.name",destination="cldr.variant.lt"),
         continent = factor(continent,labels=c("Afrika","Amerikos","Azia","Europa","Okeanija"))) 


y<- read_csv("WPP2019_Period_Indicators_Medium.csv")

y_1<- y %>%
  filter(MidPeriod %in%  1960:2018) %>%
  mutate(continent = countrycode(Location,origin="country.name",destination="continent"),
         continent = factor(continent,labels=c("Afrika","Amerikos","Azia","Europa","Okeanija")),
         LocationLT = countrycode(Location,origin="country.name",destination="cldr.variant.lt")) %>%
    drop_na(continent)


thm <- hc_theme_merge(
  hc_theme_smpl(),
  hc_theme(
    colors = c("#7cb5ec", "#434348", "#90ed7d", "#f7a35c", "#8085e9", "#f15c80", "#e4d354", "#2b908f", "#f45b5b", "#91e8e1")
  )
)

---

x_8 <- x %>% filter(Country=="World",Year == 2016)

x_8 <- x_8 %>% select(1,4:10) %>% pivot_longer(2:8) %>%
  rbind(.,x%>%
  filter(Year == 2016,Country=="World") %>% 
  select(-(2:10))  %>%
  pivot_longer(-1) %>% drop_na() %>% group_by(Country) %>% summarize(Kitos=sum(value)) %>% pivot_longer(-1))


x_9<- x %>% filter(Country=="World") %>% select(1,3:10) %>% 
  pivot_longer(3:9) %>%
  rbind(. ,x %>%
  filter(Country=="World") %>% 
  select(-(c(2,4:10)))  %>%
  pivot_longer(-(1:2)) %>% drop_na() %>% group_by(Country,Year) %>%
    summarize(Kitos=sum(value)) %>% pivot_longer(-(1:2))) %>%
  group_nest(name) %>%  
  mutate(data = map(data, mutate_mapping, hcaes(x = Year, y = value), drop = TRUE),
    data = map(data, list_parse)) %>% left_join(x_8,by=c("name"="name"))
hchart(x_9,type="column",hcaes(x=name,y=value,name=name),
       colorByPoint=TRUE,showInLegend=FALSE,pointWidth=50,
         pointPadding = 0,groupPadding=0,
       pointPlacement="on") %>%
  hc_xAxis(type="category") %>%
  hc_tooltip(pointFormatter = tooltip_chart(
    accesor = "data",
    hc_opts = list(chart = list(type="line"), credits = list(enabled = FALSE),
                   plotOptions=list(line=list(marker=list(enabled=FALSE),label=list(enabled=FALSE),lineWidth=3))
      ),
      height = 225,width=300)
  ,useHTML=TRUE) %>%
  hc_title(text="Mirties priežasčių pokytis",align="left") %>%
  hc_yAxis(title=list(text="Mirtys"),labels=list(format="{value}%")) %>%
  hc_xAxis(title=list(text="Kategorija"),tickmarkPlacement="on",min=-1,
           max=8.5,showFirstLabel=FALSE,
           showLastLabel=FALSE) %>%  hc_add_theme(thm)

---

x_10<- x %>% filter(Country == "World") %>% pivot_longer(-(1:3)) %>% drop_na() %>%
  group_by(name) %>% mutate(max = max(value)) %>% ungroup() %>% mutate(perc = value/max*100) 

hchart(x_10,type="heatmap",hcaes(x=Year,y=name,value=perc)) %>%
  hc_colorAxis(stops= color_stops(10,viridisLite::inferno(10,direction = -1,begin = 0.1)),showLastLabel=FALSE,
               labels=list(format="{value}%",size=10))%>%
  hc_size(height=750) %>%
  hc_tooltip(formatter= JS("function(){
  return this.point.x + ' ' +  this.series.yAxis.categories[this.point.y] + ': ' +
  Highcharts.numberFormat(this.point.value, 2) + '%';
}")) %>%
  hc_title(text="Mirčių kiekio palyginimas su kiekvienos kategorijos maksimumu",align="left") %>%
  hc_yAxis(title=list(text="Kategorija"),gridLineWidth=1,gridLineColor="white") %>%
  hc_xAxis(gridLineWidth=1,gridLineColor="white",title=list(text="Metai")) %>%  hc_add_theme(thm)

---

x_6 <- x_1 %>% filter(Country=="World",Year == 2016) %>% select(1:35) %>% pivot_longer(4:35) 

x_7<- x_1 %>% drop_na(countrycode) %>% select(CountryLT,2:35,marker) %>% pivot_longer(4:35) %>%
  group_by(name,Year) %>% slice_max(value,n=1) %>% nest(-name) %>%
  mutate(type="line", id = name,
    data=map(data,mutate_mapping,hcaes(x=Year,y=value)),
    data=map(data,list_parse))


hchart(x_6,type="pie",hcaes(name=name,y=value,drilldown=name),dataLabels=list(enabled=FALSE),innerSize="70%") %>%
  hc_drilldown(allowPointDrilldown = TRUE,
               series = list_parse(x_7)) %>%
  hc_plotOptions(line=list(marker=list(enabled=TRUE),tooltip=list(useHTML=TRUE,headerFormat="<b>{point.x}</b><br>",
                                        pointFormat="<b>Šalis: </b>{point.CountryLT}<br><b>Dalis mirčių: </b>{point.y}%"
                                        ))) %>%
  hc_xAxis(title=list(text="Metai")) %>%
  hc_yAxis(title=list(text="Mirtys"),labels=list(format="{value}%")) %>%
  hc_tooltip(pointFormat="<b>{point.value}%</b>",useHTML=TRUE,style=list(fontSize="15px"),
             headerFormat="<b><div style='font-size:20px;color:{point.color}'>{point.key}</div></b>") %>%
  hc_title(text="Šalys, pirmaujančios pagal mirties priežastį",align="left")  %>%  hc_add_theme(thm) 

---

which<- y_1 %>% group_by(Location) %>% select(Location,MidPeriod,LEx) %>%
  distinct(Location,MidPeriod,.keep_all = TRUE)  %>%
  pivot_wider(c(Location,MidPeriod),names_from = MidPeriod,values_from=LEx) %>%
  drop_na() %>%
  mutate(diff = `2018`-`1963`) %>% arrange(diff) %>% pull(Location)

y_9<- y_1 %>% mutate(group= ifelse(Location %in% which[1:5],"Smallest",
                                     ifelse(Location %in% rev(which)[1:5],"Largest","0"))) %>%
  filter(group!="0")


world <- y %>% filter(Location == "World") %>% select(-Location)

ggplot(y_9,aes(x=MidPeriod,y=LEx,color=continent,group=LocationLT)) + 
  geom_line(size=2) + 
  facet_wrap(vars(group,LocationLT),ncol=5,
             labeller= labeller(group=function(x) {substr(x,0,0)},Location=label_value)) +
  scale_color_viridis_d("Žemynas") + 
  theme_ipsum(base_size = 25,plot_title_size = 30,subtitle_size = 25,
              axis_title_size = 20,strip_text_size = 25,axis_text_size = 20) +
  theme(panel.grid.minor = element_blank(),legend.position = "right") +
  labs(title="Didžiausi ir mažiausi gyvenimo trukmės padidėjimai",
       subtitle="Palygininus su pasauliniu augimu") +
  xlab("Metai") + 
  ylab("Gyvenimo trukmė") + 
  scale_x_continuous(breaks=c(1960,1990,2020),limits = c(1959,2020)) +
  geom_line(data=world,inherit.aes = FALSE,aes(x=MidPeriod,y=LEx),color="black",size=2,alpha=0.2) 

---

small <- y_1 %>% filter(MidPeriod==2018) %>% mutate(lab = ifelse( (CDR < 4) | (CBR > 40) | (CDR > 12),LocationLT,""))
ggplot(subset(y_1,MidPeriod==2018),aes(CDR,CBR,color=continent)) +
  geom_vline(xintercept=8,size=1) +
  geom_hline(yintercept=27.5,size=1) +  
  geom_point(size=6) +
  ylim(5,50) +
  xlim(0,16) + 
  theme_ipsum(base_size = 25,plot_title_size = 30,
              subtitle_size = 25,axis_title_size = 20,strip_text_size = 25,axis_text_size = 20) +
  theme(panel.grid.minor = element_blank(),legend.position = "right") + 
  geom_text_repel(data=small,aes(CDR,CBR,label=lab),force = 30,inherit.aes = FALSE,size=5) +
  scale_color_viridis_d("Žemynas",guide=guide_legend(title.position = "top")) +
  theme(legend.position="bottom") +
  labs(title="Mirtys ir gimimai 1000 gyventojų 2018 metais") +
  xlab("Mirtys 1000 gyventojų") + 
  ylab("Gimimai 1000 gyventojų") 

---

y_5<- y_1 %>%
  filter(MidPeriod == 2018) %>%
  mutate(ratio = round(LExFemale/LExMale,2))%>%
  arrange(desc(ratio))

max<- y_5 %>%
  slice_max(ratio,n=10) %>%
  mutate(cat="max")

min<- y_5 %>%
  slice_min(ratio,n=10) %>%
  mutate(cat="min")

drilldown<-rbind(max,min) %>%
  group_nest(cat) %>%
  mutate(
  id = cat,
  colorKey="LEx",
  type="column",
  data=map(data,mutate_mapping,hcaes(y=ratio,name=LocationLT)),
  data=map(data,list_parse))

values <- sprintf("{point.%s}",names(y_5)[c(27,11,12,13,28)])
tltip <- tooltip_table(c("Žemynas","Gyvenimo trukmė","Vyrų gyvenimo trukmė","Moterų gyvenimo trukmė","Santykis"),values)

y_6 <- y_5 %>%
  drop_na(ratio) %>%
  filter(ratio==min(ratio)| ratio==max(ratio)) %>%
  mutate(cat=ifelse(ratio==min(ratio),"min","max"))


hchart(y_6,hcaes(name=LocationLT,y=ratio,drilldown = cat),
       type="column",colorKey="LEx",showInLegend=FALSE) %>%
  hc_colorAxis(min=59,max=80,  stops= color_stops(10,RColorBrewer::brewer.pal(n = 10,name = "Spectral")),
               title=list(text="Gyvenimo trukmė")) %>%
  hc_yAxis(min=0.8,title=list(text="Moterų ir vyrų gyvenimo trukmės santykis"),
    plotLines= list(
      list(
        label = list(text = ""),
        color = "#FF0000",
        width = 2,
        value = 1,
        zIndex = 0
      ))) %>%
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series = list_parse(drilldown)
  ) %>%
  hc_xAxis(type="category",title=list(text="Šalis")) %>%
  hc_tooltip(pointFormat=tltip,useHTML=TRUE,zIndex=11) %>%
  hc_title(text="Šalis su didžiausiu ir mažiausiu moterų ir vyrų gyvenimo trukmės santykiu 2018 metais",align="left") %>% 
  hc_add_theme(thm)

---

y_8 <- y_1 %>% filter(MidPeriod == 2018) %>%
  mutate(ratio2 = round(DeathsMale/DeathsFemale,5),
         ratio = round(LExFemale/LExMale,5))


ggplot(y_8,aes(ratio,ratio2,color=continent)) +
  geom_jitter(width = 0.002,height=0.002,size=8.5,alpha=0.8) +
  geom_text_repel(data=subset(y_8,ratio2>1.8 | ratio2<1 | ratio > 1.14),
                  aes(label=LocationLT),show.legend = FALSE,size=7,force = 2,nudge_y = -0.3,nudge_x = -0.003) + 
  ylim(-0.2,3.5) +
  xlim(0.98,1.2) + 
  geom_hline(yintercept=1,color="black") + 
  annotate(geom = "segment",x=1.1,y=0.3,xend=1.2,yend=0.3,
           arrow = arrow(length = unit(0.5, "cm")),size=1) + 
  geom_vline(xintercept=1,color="black") +
  annotate(geom="text",x=1.15,y=0,label="Didesnė moterų gyvenimo trukmė",size=8) + 
  annotate(geom = "segment",y=1.3,x=0.99,yend=3,xend=0.99,
           arrow = arrow(length = unit(0.5, "cm")),size=1) +
  scale_color_viridis_d("Žemynas",guide=guide_legend(title.position = "top")) +
  labs(title="Gyvenimo trukmės ir mirčių santykiai 2018 metais") +
  xlab("Gyvenimo trukmės santykis") +
  ylab("Mirčių santykis") +
  annotate(geom="text",x=0.983,y=2,label="Daugiau vyrų mirčių",angle=90,size = 8) +
  theme_ipsum(base_size = 25,plot_title_size = 30,axis_title_size = 20) +
  theme(panel.grid.minor = element_blank(),legend.position = "bottom")

---

values <- c("{point.CountryLT}",paste0(sprintf("{point.%s}",names(x_1)[c(18,27)]),"%"))
tltip <- tooltip_table(c("Šalis",names(x_1)[c(18,27)]),values)


x_2 <- x_1 %>%
  filter(Year == 2016) %>%
  drop_na(countrycode,continent)
highchart() %>%
  hc_add_series(x_2,type="scatter",hcaes(Alkoholis,Savižudybė,group=continent),
                marker = list(symbol="circle"),
                states = list(hover=list(halo=list(size=20,opacity=0.55)))) %>%
  hc_title(text="Mirčių dalis dėl alkoholio ir savyžudybių 2016 metais",align="left") %>%
  hc_tooltip(pointFormat = tltip,useHTML=TRUE) %>%
  hc_xAxis(title=list(text="Mirtys dėl alkoholio"),
           minRange = 1,labels=list(format="{value}%")) %>%
  hc_yAxis(title=list(text="Mirtys dėl savižudybės"),
           minRange=1,labels=list(format="{value}%")) %>%
  hc_legend(verticalAlign="middle",align="right",layout="vertical") %>%
  hc_chart(zoomType="xy") %>% hc_add_theme(thm)

---

values <- c("{point.CountryLT}",paste0(sprintf("{point.%s}",names(x_1)[c(17,21)]),"%"))
tltip <- tooltip_table(c("Šalis",names(x_1)[c(17,21)]),values)


x_2 <- x_1 %>%
  filter(Year == 2016) %>%
  drop_na(countrycode,continent) 
highchart() %>%
  hc_add_series(x_2,type="scatter",hcaes(x=Neprievalgis,y=AIDS,group=continent),
                marker = list(symbol="circle"),
                states = list(hover=list(halo=list(size=20,opacity=0.55)))) %>%
  hc_title(text="Mirčių dalis dėl neprievalgio ir ŽIV/AIDS 2016 metais",align="left") %>%
  hc_tooltip(pointFormat = tltip,useHTML=TRUE) %>%
  hc_xAxis(title=list(text="Mirtys nuo neprievalgio"),
           minRange=1,labels=list(format="{value}%")) %>%
  hc_yAxis(title=list(text="Mirtys nuo ŽIV/AIDS"),
           minRange=1,labels=list(format="{value}%")) %>%
  hc_legend(verticalAlign="middle",align="right",layout="vertical") %>%
  hc_chart(zoomType="xy") %>%  hc_add_theme(thm)

---

y_2 <- y %>% filter(Location %in% c("World","Africa","Asia","Europe","Oceania","South America","Northern America")) %>%
  filter(MidPeriod %in% 1960:2050) %>%
  mutate(Location = factor(Location,
                           labels=c("Afrika","Azia","Europa","Šiaurės Amerika","Okeanija","Pietų Amerika","Pasaulis")))


y_3<-y_2 %>% distinct(Location,MidPeriod,.keep_all = TRUE)  %>% select(Location,CDR,CBR) %>% 
    group_by(Location) %>%
    do(data=list(sequence=list(.$CDR,.$CBR))) %>% pull(data) %>% map(~map(.x,transpose))

y_3<- map(y_3,~list(.x))

y_4<-y_2 %>% select(Location) %>% rename(name=Location) %>%
  unique()
y_4$data <- y_3
y_4<- list_parse(y_4)


highchart() %>%
  hc_yAxis(max =50, min = 0,title=list(text="Gimimai 1000 gyventojų")) %>% 
  hc_xAxis(max =25, min = 0,title=list(text="Mirtys 1000 gyventojų")) %>%
  hc_chart(type="scatter") %>%
  hc_plotOptions(scatter=list(marker = list(symbol="circle",radius=8),zIndex=10)) %>%
  hc_add_series_list(y_4) %>%
  hc_motion(
    enabled=TRUE,
    labels=unique(y_2$MidPeriod),
    series = 0:6,
    updateInterval = 10
  ) %>%
  hc_legend(align="right") %>%
  hc_title(text="Metiniai gimimai ir mirtys 1000 gyventojų",align='left') %>%
  hc_subtitle(text="Įskaitant ateities projekcijas",align="left") %>%
  hc_tooltip(pointFormat="<b>Mirtys:</b> {point.x}<br><b>Gimimai:</b> {point.y}",style=list(fontSize="15px")) %>%
  hc_add_series(tibble(x=c(0,25),y=c(0,25)),type="line",hcaes(x=x,y=y),
                color="black",zIndex=0,marker=list(enabled=FALSE),
                states=list(hover=list(enabled=FALSE)),
                showInLegend=FALSE,opacity=0.6,
                tooltip=list(headerFormat="Lygybė<br>",useHTML=TRUE)) %>%
   hc_annotations(list(draggable="",
                      labelOptions = list(padding=10,backgroundColor = "transparent",style=list(color="black")),
                      labels = list(list(point=list(x=20,y=22,xAxis=0,yAxis=0),
                                         text="Mirčių ir gimimų lygybė<br>")))) %>% 
  hc_add_theme(thm) %>%
  hc_size(width=1000)

---

x_3 <- x %>%
  filter(Year == 2016) %>%
  filter(str_detect(Country,"SDI"))
x_3<- x_3 %>% select(1,4:10,"AIDS") %>% pivot_longer(2:9)

x_4<- x %>%
    filter(Year == 2016) %>%
    filter(str_detect(Country,"SDI")) %>% select(-(2:10),-"AIDS")  %>%
  pivot_longer(-1) %>% drop_na() %>% group_by(Country) %>%
  summarize(Kitos=sum(value)) %>%
  pivot_longer(-1)

x_3 <- rbind(x_3,x_4)
x_3$Country <- factor(x_3$Country,
                      labels=c("Vidutinis-aukštas SDI","Aukštas SDI","Vidutinis-žemas SDI","Žemas SDI","Vidutinis SDI"))

highchart() %>%
  hc_add_series(x_3,type="line",hcaes(x=name,y=value,group=Country)) %>%
  hc_xAxis(type="category") %>%
  hc_chart(polar=TRUE) %>%
  hc_title(text="Mirtingumo profilis pagal sociodemografinį indeksą",align="left") %>%
  hc_tooltip(useHTML=TRUE) %>%
  hc_yAxis(labels=list(format="{value}%")) %>%hc_add_theme(thm)

---

y_7<-left_join(x_1 %>%
  filter(Year == 2016) %>%
  drop_na(countrycode,continent),subset(y_1,MidPeriod==2018),by=c("Country"="Location")) %>%
  drop_na(LEx) %>%
  mutate(levels = cut_interval(LEx,n=4,labels=c("Žema","Vidutinė-žema","Vidutinė-aukšta","Aukšta"))) %>% select(1,4:35,levels) %>%
  mutate(across(everything(),~replace_na(.x,0))) %>% pivot_longer(2:33) %>% group_by(levels,name) %>%
  summarize(m = mean(value)) %>% ungroup() %>% group_by(levels) %>% mutate(n=sum(m),m=round(m/n*100,2)) %>%
  group_by(levels) %>% arrange(.by_group = TRUE,desc(m)) %>% arrange(desc(levels)) %>% ungroup() %>%
  nest(-name) %>%
  mutate(data=map(data,mutate_mapping,hcaes(name=levels,y=m)),
         data=map(data,list_parse),
         type="bar",
         showInLegend=FALSE)


highchart() %>%
  hc_chart(type="bar") %>%
  hc_plotOptions(bar=list(stacking="normal",opacity=0.8,states=list(hover=list(opacity=1,borderColor="black")))) %>%
  hc_add_series_list(list_parse(y_7)) %>% 
  hc_yAxis(title=list(text="Mirčių dalis"),labels=list(format="{value}%")) %>%
  hc_xAxis(type="category",
           categories=c("Žema","Vidutinė-žema","Vidutinė-aukšta","Aukšta"),title=list(text="Vidutinė gyvenimo trukmė")) %>%
  hc_title(align="left",
           text="Mirtingumo profilis pagal vidutinę gyvenimo trukmę") %>% hc_add_theme(thm) %>%
  hc_tooltip(style=list(fontSize="20px"),
             useHTML=TRUE,
             pointFormat="<b><div style='font-size:20px;color:{point.color}'>{series.name}: {point.m}%</b>",
             headerFormat="<span style='font-size:16px'>{point.key} gyvenimo trukmė</span>")

y_ratio_s<- y_1 %>%
  filter(MidPeriod== 2018) %>%
  select(LocationLT, SRB, continent) %>%
  distinct(LocationLT, SRB, continent) %>%
  drop_na(SRB)

iskirtisUP<- y_ratio_s %>%
  filter(SRB>1.1 | LocationLT == "Lietuva") %>%
  arrange(desc(SRB)) 

iskirtisUP %>%
  hchart('bar', hcaes(x = LocationLT, y = SRB), dataLabels = list(enabled = TRUE, format = "{point.SRB}")) %>%
  hc_title(text="Šalys su dideliu vyrų ir moterų nežinau kaip pavadinti", margin = 20, align = "left") %>%
  hc_subtitle(text="compared to average",align="left") %>%
  hc_yAxis(min = 1, title=list(text="men/women ratio at birht")) %>% 
  hc_xAxis(title=list(text="country")) %>% 
  hc_tooltip(pointFormat = '{series.x} ratio: {point.y:.3f}<br/>{point.continent}<br/>')

gdata<- y_1 %>%
  drop_na(continent) %>%
  drop_na(Q5) %>%
  drop_na(MAC) %>%
  drop_na(IMR) %>%
  select(Q5, IMR, MAC, continent, MidPeriod)

g <-plyr::ddply(gdata,c("continent","MidPeriod"),summarise,mean=mean(MAC))
g %>% 
  hchart(
    'line', hcaes(x = MidPeriod, y = mean, group = continent),
    marker = list(symbol="circle")
  )  %>%
  hc_colors(c("#AC92EB", "#4FC1E8", "#A0D568", "#FFCE54", "#ED5564")) %>%
  hc_title(text="Female mean age of childbearingąčęėį",align="left") %>%
  hc_xAxis(title=list(text="Year")) %>%
  hc_yAxis(title=list(text="Age"))

g2 <-plyr::ddply(gdata,c("continent","MidPeriod"),summarise,suma=sum(Q5))
g2 %>% 
  hchart(
    'line', hcaes(x = MidPeriod, y = suma, group = continent),
    marker = list(symbol="circle")
  )  %>%
  hc_colors(c("#AC92EB", "#4FC1E8", "#A0D568", "#FFCE54", "#ED5564")) %>%
  hc_title(text="Mortality of kids under 5 by regionąčęėįšų",align="left") %>%
  hc_xAxis(title=list(text="Year")) %>%
  hc_yAxis(title=list(text="Deaths"))

g3 <-plyr::ddply(gdata,c("continent","MidPeriod"),summarise,suma=sum(IMR))
g3 %>% 
  hchart(
    'line', hcaes(x = MidPeriod, y = suma, group = continent),
    marker = list(symbol="circle")
  )  %>%
  hc_colors(c("#AC92EB", "#4FC1E8", "#A0D568", "#FFCE54", "#ED5564")) %>%
  hc_title(text="Mortality of infant kids by region",align="left") %>%
  hc_xAxis(title=list(text="Year")) %>%
  hc_yAxis(title=list(text="Deaths"))